Is There a Higher-Order Mechanism that Explains Performance Across Prediction Tasks?

People constantly make predictions about what will happen in the near future. People anticipate how other people around them will act, what other people will say, and what actions will help them achieve the greatest rewards. Because all of these behaviors are typically called prediction, it is easy to make the assumption that performance across all of these types of tasks is driven by the same underlying mechanism. However, there has been little investigation into whether the mechanisms underlying prediction are the same across multiple task modalities. Therefore, in the current study, 226 participants completed four types of tasks that putatively involve prediction to determine whether there is a common factor that can account for performance on these tasks. Fluid and crystallized intelligence were also assessed to ensure that general intelligence did not drive correlations among the tasks. Preliminary evidence from a recent study suggested that people with Posttraumatic Stress Disorder (PTSD) have difficulty with predicting future activity; therefore, participants also completed a questionnaire screening for symptoms of PTSD. Performance across the four prediction tasks was not correlated, and PTSD severity was not significantly correlated with any of the tasks in the study. These results suggest that there is not an integrative prediction mechanism in the brain, but rather that there are multiple prediction systems operating in parallel within the brain. In addition, these results suggest that PTSD may only be associated with a subset, if any, of prediction tasks. Future researchers studying prediction must be careful to investigate performance on various prediction tasks separately, rather than assuming that prediction performance is stable across tasks

Towards Robust Machine Learning for Health Applications

Methoden des maschinellen Lernens haben über die letzten Jahrzehnte beeindruckende technologische Fortschritte ermöglicht und haben das Potenzial, viele Aspekte unseres Lebens nachhaltig zu verändern. Besonders vielversprechend ist maschinelles Lernen im Gesundheitsbereich. Hier kann es unser Verständnis immer komplexerer Gesundheitsdaten vertiefen, Prozesse wie Diagnostik und Risikoeinschätzung beschleunigen sowie deren Objektivität erhöhen, und eine personalisiertere medizinische Versorgung ermöglichen. Zugleich steht maschinelles Lernen im Gesundheitsbereich vor besonderen Herausforderungen. Gesundheitsdaten sind häufig zeitabhängig und heterogen, über mehrere Institutionen verteilt und nur in begrenztem Umfang für spezifische Modellierungsanwendungen zugänglich. Infolgedessen erfordert das maschinelle Lernen für den Gesundheitsbereich grundsätzlich robuste Methoden, die für heterogene und im Umfang begrenzte Daten geeignet sind, sowie besonders auf die jeweilige Anwendung zugeschnittene Modelle. Diese Dissertation umfasst Beiträge zu beiden dieser Aspekte. Sie enthält neue Methoden zur unüberwachten Domänenadaptation, die speziell für hochdimensionale molekulare Gesundheitsdaten entwickelt wurden und eine genauere Vorhersage über heterogene Datensätze hinweg ermöglichen. Als konkretes Anwendungsbeispiel wurden diese Methoden auf das Problem der Altersvorhersage basierend auf DNA-Methylierungsdaten über Gewebe hinweg angewandt. Im Vergleich zu einem nicht-adaptiven Referenzmodell verbesserten sie hierbei die Vorhersage auf einem Gewebe, das nicht zum Trainieren der Modelle verwendet wurde. Zusätzlich enthält diese Dissertation robuste Modelle zur Analyse von Daten einer frühen klinischen Studie, die die Verwendung von breitneutralisierenden Antikörpern zur Behandlung von HIV untersuchte. Hier wurden Modelle und Methoden gewählt, die trotz des begrenzten Stichprobenumfangs Heterogenität zwischen Patientengruppen berücksichtigen konnten. Ein weiterer anwendungsspezifischer Beitrag war die Entwicklung robuster Modelle zur zeitabhängigen Vorhersage der Mortalität sowie einer Cytomegalievirus-Reaktivierung nach hämatopoetischer Stammzelltransplantation. Diese Modelle wurden in einer prospektiven, nicht-interventionellen klinischen Studie validiert und generierten in einem Pilot-Vergleich eine ähnliche genaue Vorhersage wie die Einschätzung erfahrener Kliniker. Zusätzlich unterstützte diese Dissertation die Entwicklung der XplOit-Plattform, einer Software-Plattform, die robustes maschinelles Lernen für den Gesundheitsbereich durch die semantische Integration heterogener Daten erleichtert.Machine learning has enabled striking technological advances over the last decades and has the potential to transform many aspects of our lives. Its application is especially promising in the health domain, where it can improve our understanding of increasingly complex health data, accelerate processes such as diagnosis or risk assessment while also making them more objective, and enable a more personalized approach to medicine. At the same time, machine learning for health faces particular challenges. Health data is often temporal and heterogeneous, distributed across many institutions, and accessible only in modest amounts for a specific machine learning application. Consequently, machine learning for health requires generally robust methods capable of handling heterogeneous and limited data and models that are well-tailored to the task at hand. This thesis contributes to both of these aspects. It includes new methods for unsupervised domain adaptation, which were designed for high-dimensional molecular health data and improved prediction across heterogeneous datasets. As a concrete application example, these methods were applied to the problem of age prediction from DNA methylation data across tissues, where they improved age prediction on a tissue not used for model training compared to a non-adaptive reference model. In addition, this thesis includes robust models for the analysis of data from an early clinical trial evaluating the use of broadly neutralizing antibodies for the treatment of HIV, which were suitable to account for heterogeneity between patient groups despite a limited sample size. Another application-specific contribution was the development of robust models for the time-dependent prediction of mortality and early cytomegalovirus reactivation after hematopoietic cell transplantation. These models were validated in a prospective non-interventional clinical trial and demonstrated similar performance as experienced physicians in a pilot comparison. Finally, this thesis supported the development of the XplOit platform, a software platform that facilitates robust machine learning for health by semantically integrating heterogeneous datasets

Uncovering the Mechanism of Aggregation of Human Transthyretin.

The tetrameric thyroxine transport protein transthyretin (TTR) forms amyloid fibrils upon dissociation and monomer unfolding. The aggregation of transthyretin has been reported as the cause of the life-threatening transthyretin amyloidosis. The standard treatment of familial cases of TTR amyloidosis has been liver transplantation. Although aggregation-preventing strategies involving ligands are known, understanding the mechanism of TTR aggregation can lead to additional inhibition approaches. Several models of TTR amyloid fibrils have been proposed, but the segments that drive aggregation of the protein have remained unknown. Here we identify β-strands F and H as necessary for TTR aggregation. Based on the crystal structures of these segments, we designed two non-natural peptide inhibitors that block aggregation. This work provides the first characterization of peptide inhibitors for TTR aggregation, establishing a novel therapeutic strategy

Understanding Strategically Important Populations: Assessing the Information Practices, Needs, and Perceptions of International Graduate Students

Objective: International student enrollment in the United States has grown considerably in the last decade. International graduate students now represent a significant portion of graduate students in the United States and graduate students represent a significant portion of the total number of international students. Colleges and universities increasingly rely on international students for tuition and fee revenue, which is often significantly higher than the tuition and fees paid by domestic students. International enrollment is not evenly distributed across institutions or geographic areas and indeed international enrollment is quite prominent at some institutions. Only eight universities – one of which is the University of Illinois at Urbana-Champaign – have more than 10,000 international students and as such, international students represent a strategically important population at the University of Illinois Library at Urbana-Champaign. Methods: To gain insights into the information practices, needs, and perceptions of the international graduate students at Illinois, the University Library partnered with Ithaka S+R to develop an international module for the Ithaka S+R Graduate/Professional Student Survey to gather additional data from the international students. Results: Some of the findings from the survey confirm results previously reported in the literature, while other findings raise questions about commonly-held beliefs about language difficulties and prior library experience shaping library use and research practices. In addition, this is the first known library user study to investigate the impact of whether students completed their undergraduate degrees in the United States or in another country on their information behavior and perceptions. Conclusion: This session will share findings from the survey and how those findings can inform the Library’s service strategy for a strategically important population. Session attendees have the opportunity to reflect on their own institutions’ strategically important populations and their libraries’ efforts to meet their needs.Ope

A Pilot Study of Sidewalk Equity in Seattle Using Crowdsourced Sidewalk Assessment Data

We examine the potential of using large-scale open crowdsourced sidewalk data from Project Sidewalk to study the distribution and condition of sidewalks in Seattle, WA. While potentially noisier than professionally gathered sidewalk datasets, crowdsourced data enables large, cross-regional studies that would be otherwise expensive and difficult to manage. As an initial case study, we examine spatial patterns of sidewalk quality in Seattle and their relationship to racial diversity, income level, built density, and transit modes. We close with a reflection on our approach, key limitations, and opportunities for future work.Comment: Workshop paper presented at "The 1st ASSETS'22 Workshop on The Future or urban Accessibility (UrbanAccess'22)

Aggregation-triggering segments of SOD1 fibril formation support a common pathway for familial and sporadic ALS

ALS is a terminal disease of motor neurons that is characterized by accumulation of proteinaceous deposits in affected cells. Pathological deposition of mutated Cu/Zn superoxide dismutase (SOD1) accounts for ∼20% of the familial ALS (fALS) cases. However, understanding the molecular link between mutation and disease has been difficult, given that more than 140 different SOD1 mutants have been observed in fALS patients. In addition, the molecular origin of sporadic ALS (sALS) is unclear. By dissecting the amino acid sequence of SOD1, we identified four short segments with a high propensity for amyloid fibril formation. We find that fALS mutations in these segments do not reduce their propensity to form fibrils. The atomic structures of two fibril-forming segments from the C terminus, ^(101)DSVISLS^(107) and ^(147)GVIGIAQ^(153), reveal tightly packed β-sheets with steric zipper interfaces characteristic of the amyloid state. Based on these structures, we conclude that both C-terminal segments are likely to form aggregates if available for interaction. Proline substitutions in 101DSVISLS107 and ^(147)GVIGIAQ^(153) impaired nucleation and fibril growth of full-length protein, confirming that these segments participate in aggregate formation. Our hypothesis is that improper protein maturation and incompletely folded states that render these aggregation-prone segments available for interaction offer a common molecular pathway for sALS and fALS

Clinical and biochemical effects of a combination botanical product (ClearGuard™) for allergy: a pilot randomized double-blind placebo-controlled trial

<p>Abstract</p> <p>Background</p> <p>Botanical products are frequently used for treatment of nasal allergy. Three of these substances, <it>Cinnamomum zeylanicum</it>, <it>Malpighia glabra</it>, and <it>Bidens pilosa</it>, have been shown to have a number of anti-allergic properties <it>in-vitro</it>. The current study was conducted to determine the effects of these combined ingredients upon the nasal response to allergen challenge in patients with seasonal allergic rhinitis.</p> <p>Methods</p> <p>Twenty subjects were randomized to receive the combination botanical product, (CBP) 2 tablets three times a day, loratadine, 10 mg once a day in the morning, or placebo, using a randomized, double-blinded crossover design. Following 2 days of each treatment and during the third day of treatment, subjects underwent a nasal allergen challenge (NAC), in which nasal symptoms were assessed after each challenge dose and every 2 hours for 8 hours. Nasal lavage fluid was assessed for tryptase, prostaglandin D2, and leukotriene E4 concentrations and inflammatory cells.</p> <p>Results</p> <p>Loratadine significantly reduced the total nasal symptom score during the NAC compared with placebo (P = 0.04) while the CBP did not. During the 8 hour period following NAC, loratadine and the CBP both reduced NSS compared with placebo (P = 0.034 and P = 0.029, respectively). Analysis of nasal lavage fluid demonstrated that the CBP prevented the increase in prostaglandin D2 release following NAC, while neither loratadine nor placebo had this effect. None of the treatments significantly affected tryptase or leukotriene E4 release or inflammatory cell infiltration.</p> <p>Conclusion</p> <p>The CBP significantly reduced NSS during the 8 hours following NAC and marginally inhibited the release of prostaglandin D2 into nasal lavage fluid, suggesting potential clinical utility in patients with allergic rhinitis.</p

Dynamic Diagnosis of Familial Prion Diseases Supports the β2-α2 Loop as a Universal Interference Target

[Background] Mutations in the cellular prion protein associated to familial prion disorders severely increase the likelihood of its misfolding into pathogenic conformers. Despite their postulation as incompatible elements with the native fold, these mutations rarely modify the native state structure. However they variably have impact on the thermodynamic stability and metabolism of PrPC and on the properties of PrPSc aggregates. To investigate whether the pathogenic mutations affect the dynamic properties of the HuPrP(125-229) α-fold and find possible common patterns of effects that could help in prophylaxis we performed a dynamic diagnosis of ten point substitutions.[Methodology/Principal Findings] Using all-atom molecular dynamics simulations and novel analytical tools we have explored the effect of D178N, V180I, T183A, T188K, E196K, F198S, E200K, R208H, V210I and E211Q mutations on the dynamics of HuPrP(125-228) α-fold. We have found that while preserving the native state, all mutations produce dynamic changes which perturb the coordination of the α2-α3 hairpin to the rest of the molecule and cause the reorganization of the patches for intermolecular recognition, as the disappearance of those for conversion inhibitors and the emergence of an interaction site at the β2-α2 loop region.[Conclusions/Significance] Our results suggest that pathogenic mutations share a common pattern of dynamical alterations that converge to the conversion of the β2-α2 loop into an interacting region that can be used as target for interference treatments in genetic diseases.This work was supported in parts by grants BFU2009-07971 from the MICINN (MG), FundaciÃ3n Cien (MG); Fondazione Cariplo (GC) and AIRC (GC). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. No additional external funding received for this study.Peer reviewe

Character pathology and neuropsychological test performance in remitted opiate dependence

<p>Abstract</p> <p>Background</p> <p>Cognitive deficits and personality pathology are prevalent in opiate dependence, even during periods of remission, and likely contribute to relapse. Understanding the relationship between the two in vulnerable, opiate-addicted patients may contribute to the design of better treatment and relapse prevention strategies.</p> <p>Methods</p> <p>The Millon Multiaxial Clinical Inventory (MCMI) and a series of neuropsychological tests were administered to three subject groups: 29 subjects receiving methadone maintenance treatment (MM), 27 subjects in protracted abstinence from methadone maintenance treatment (PA), and 29 healthy non-dependent comparison subjects. Relationships between MCMI scores, neuropsychological test results, and measures of substance use and treatment were examined using bivariate correlation and regression analysis.</p> <p>Results</p> <p>MCMI scores were greater in subjects with a history of opiate dependence than in comparison subjects. A significant negative correlation between MCMI scores and neuropsychological test performance was identified in all subjects. MCMI scores were stronger predictors of neuropsychological test performance than measures of drug use.</p> <p>Conclusion</p> <p>Formerly methadone-treated opiate dependent individuals in protracted opiate abstinence demonstrate a strong relationship between personality pathology and cognitive deficits. The cause of these deficits is unclear and most likely multi-factorial. This finding may be important in understanding and interpreting neuropsychological testing deficiencies in opiate-dependent subjects.</p